A Fuzzy-Based PI Controller for Power Management of a Grid-Connected PV-SOFC Hybrid System

Sukumar, Shivashankar; Marsadek, Marayati; Ramasamy, Agileswari K.; Mokhlis, Hazlie; Mekhilef, Saad

doi:10.3390/en10111720

Cited by 19 publications

(10 citation statements)

References 29 publications

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“…Considerable research has been conducted on inverter control issues, internal power losses, system nonlinearity, power fluctuation, environmental variation, low PV efficiency, harmonic distortion, DC-bus voltage fluctuation, electromagnetic interface, and unity power factor maintenance [102]. Therefore, developing an efficient inverter controller to enhance the overall performance of a PV system is challenging [103]- [107]. A number of PID and PI controllers has been implemented in PV inverter systems [100].…”

Section: Inverter Control Strategiesmentioning

confidence: 99%

“…PI controllers are commonly employed in inverter controls [31]. However, these controllers require a trapezoidal sum approximation to transform the function from the continuous-time to the discrete-time domain for suitable processing and performance [103]. Optimization techniques have been recommended to tune the gains of the PI controller and improve the performance [148].…”

Section: Inverter Controller and Its Algorithmmentioning

confidence: 99%

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Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

et al. 2019

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Fossil fuels and other conventional energy sources used to generate electricity are finite. Therefore, alternative energy sources should be pursued to meet present and future energy demands. The photovoltaic (PV) is a promising renewable energy source, especially for the remote areas. The PV is a DC power source that needs to be converted into usable AC power using an inverter. However, its nonlinearity and output fluctuation pose challenges in the design of PV based inverter. In this paper, a PV inverter controller system with the fundamentals of a fuzzy logic controller (FLC) and its applications and execution are reviewed. The different fuzzy controllers, inverter control algorithms, and switching techniques are studied. The findings indicate that the fuzzy logic controls have been gaining attention in the area of power control engineering, especially in inverter controller design for PV applications and generation. The FLC has a flexible and intelligent design, expedient user interface, easy computation and learning system, and combinations of different control algorithms. The FLC is also verifiable for completeness, redundancy, and consistency. However, finding the boundaries of membership functions and other rules of FLC requires manual tuning, long computation time, and considerable effort. This paper comprehensively reviews the FLC-based inverter control system to minimize PV output fluctuations, which cause inverter issues related to output harmonics, power factor, switching schemes, losses, and system implementation. The inverter system and its control strategy for future PV applications and generation require further research and development. Consequently, this review focuses on many factors and challenges and provides recommendations for designing capable and efficient inverter control systems for converting PV power to usable AC power. All the highlighted insights of this review will hopefully lead to increased efforts toward the development of the advanced inverter control systems for PV applications for AC loads and the utility grid.

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Section: Inverter Control Strategiesmentioning

confidence: 99%

Section: Inverter Controller and Its Algorithmmentioning

confidence: 99%

Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

et al. 2019

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“…Fuzzy Logic control is a range to point or range-to-range control. Fuzzy Logic techniques is applied throughout many purposes [30][31][32]. Fuzzy logic implementations involve three essential steps: the first is Fuzzification, this process to convert crisp values to fuzzy data using membership functions (MF), second is Inference process, to process MF within sets of rules or laws to develop the fuzzy output and third is Defuzzification [33], to determine the correct output.…”

Section: Algorithm Developmentmentioning

confidence: 99%

Fuzzy Control for Smart PV-Battery System Management to Stabilize Grid Voltage of 22 kV Distribution System in Thailand

2018

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This paper presents a fuzzy logic based algorithm developed to bring smart functionality to an ordinary PV-battery system in order to maintain the grid voltage stability of the 22 kV distribution system in Thailand. This research focuses on minimizing grid voltage fluctuations by converting a typical PV system into a smart PV-battery system (SPVs-BSS). A SPVs-BSS will be able to control the electrical power from a PV system to maintain the grid voltage in case of unexpected events or emergencies. Grid support functions such as a variable reactive power control and active power control will be discussed, leading to strategies for charging and discharging the battery system in response to the status of grid voltage. Fuzzy Logic was used to develop this control algorithm, which is named the Voltage Stability Fuzzy Logic Algorithm (VSFL Algorithm). The methodology of this research consists of three parts. First, testing the grid inverter operated on grid support functions. Second, the VSFL algorithm was developed to manage both the grid inverter and the battery system. Third, a SPVs-BSS equipped with the VSFL algorithm was simulated by using DIgsilent PowerFactory software. Results showed that the SPVs-BSS equipped with the VSFL Algorithm successfully maintained grid voltage in target range.

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“…MPP and MEP are usually sought by implementing a tracker that controls the hydrogen and air regulator so that the efficiency or power of the PEMFC is maximized. Different approaches are used to control MPPT/MPET, which can be divided into perturb and observe algorithms [43][44][45], fuzzy logic control [46][47][48], sliding mode control [49][50][51], and extremum seeking control [52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Renewable/Fuel Cell Hybrid Power System Operation Using Two Search Controllers of the Optimal Power Needed on the DC Bus

et al. 2020

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In this paper, the optimal and safe operation of a hybrid power system based on a fuel cell system and renewable energy sources is analyzed. The needed DC power resulting from the power flow balance on the DC bus is ensured by the FC system via the air regulator or the fuel regulator controlled by the power-tracking control reference or both regulators using a switched mode of the above-mentioned reference. The optimal operation of a fuel cell system is ensured by a search for the maximum of multicriteria-based optimization functions focused on fuel economy under perturbation, such as variable renewable energy and dynamic load on the DC bus. Two search controllers based on the global extremum seeking scheme are involved in this search via the remaining fueling regulator and the boost DC–DC converter. Thus, the fuel economy strategies based on the control of the air regulator and the fuel regulator, respectively, on the control of both fueling regulators are analyzed in this study. The fuel savings compared to fuel consumed using the static feed-forward control are 6.63%, 4.36% and 13.72%, respectively, under dynamic load but without renewable power. With renewable power, the needed fuel cell power on the DC bus is lower, so the fuel cell system operates more efficiently. These percentages are increased to 7.28%, 4.94% and 14.97%.

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A Fuzzy-Based PI Controller for Power Management of a Grid-Connected PV-SOFC Hybrid System

Cited by 19 publications

References 29 publications

Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

Fuzzy Control for Smart PV-Battery System Management to Stabilize Grid Voltage of 22 kV Distribution System in Thailand

Renewable/Fuel Cell Hybrid Power System Operation Using Two Search Controllers of the Optimal Power Needed on the DC Bus

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